/* libwatopt++ - Optimization Toolkit
 *
 * Copyright (c) 2007 Christopher Alexander Watford
 * <christopher.watford@gmail.com>
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
 * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
 * IN THE SOFTWARE.
 *
 * $Id: backprop_network.hpp 2 2007-05-06 18:01:15Z christopher.watford $
 */

#if _MSC_VER > 1000
#pragma once
#endif /* _MSC_VER > 1000 */

#ifndef _BACKPROP_NETWORK_HPP_
#define _BACKPROP_NETWORK_HPP_

#include <cstdlib>

namespace watopt
{

   class NN_BackPropagate
   {
   public:
	   NN_BackPropagate(size_t inputs, size_t hidden, size_t output, double rate);
	   ~NN_BackPropagate(void);

	   void InitializeWeights(void);
	   void TrainWithSet(const double *inputs, const double *outputs, size_t trainingSize, bool randomize = true);
	   void TrainWithValues(const double *inputs, const double *outputs);

	   void operator() (const double *inputs, double *hiddenOutput, double *outputs) const;

   private:
	   size_t	_m_InputNodes;
	   size_t	_m_HiddenNodes;
	   size_t	_m_OutputNodes;

	   double	_m_LearningRate;

	   double	*_m_HiddenWeights;	/* node x input */
	   double	*_m_OutputWeights;	/* output x node */
	   double	*_m_Bias;			/* node */
	   double	*_m_HiddenDelta;	/* node x input */
	   double	*_m_OutputDelta;	/* output x node */
	   double	*_m_BiasDelta;		/* node */

	   double	*__hiddenOutput;
	   double	*__output;
	   double	*__err;
   };

}

#endif /* !defined(_BACKPROP_NETWORK_HPP_) */